摘要：

针对在线评论情感分析的复杂特征抽取问题，提出一种基于粗糙集的在线评论情感分析模型。分析传统词袋性特征，指出固定搭配特征在情感极性判别中的作用，采用粗糙集方法挖掘在线评论中的固定搭配特征，将其融合于SVM与Naive Bayes等情感分析模型中。实际酒店的在线评论情感分析结果表明，增加粗规则后，SVM模型与Naive Bayes模型获得的评论情感判别精度都有所提高。

关键词: 情感分析, 粗糙集, 特征提取, 词袋特征, 固定搭配特征, 支持向量机

Abstract:

In order to extract the complicated features for online reviews sentiment analysis, an online reviews sentiment analysis model based on rough sets is presented. It analyzes the traditional word bags features, and the regular collocation features are confirmed to play important role in identifying the reviews sentiment. The regular collocation features as rough rules extracted are incorporated into the sentiment analysis model, and the results on real hotel online reviews show the performance improvement under both Support Vector Machine(SVM) model and Naive Bayes model.

Key words: sentiment analysis, rough set, feature extraction, word bag feature, regular collocation feature, Support Vector Machine(SVM)

中图分类号: 

• TP391